





"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		dict     ):







      '''simple docstring'''




      return (data["data"], data["target"])


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		np.ndarray				,		SCREAMING_SNAKE_CASE	:		np.ndarray     ):







      '''simple docstring'''




      lowerCAmelCase    		=			XGBRegressor(verbosity=0				,		random_state=42     )
      xgb.fit(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      # Predict target for test data
      lowerCAmelCase    		=			xgb.predict(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			predictions.reshape(len(SCREAMING_SNAKE_CASE     )				,		1     )
      return predictions


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			fetch_california_housing()
      lowerCAmelCase		,						lowerCAmelCase    		=			data_handling(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			train_test_split(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		test_size=0.25				,		random_state=1     )
      lowerCAmelCase    		=			xgboost(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      # Error printing
      print(F'Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}'     )
      print(F'Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )}'     )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()













"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






